A Switch State Recognition Method based on Improved VGG19 network

Yong-jun, Xia; Cai, Min; Ni, Chuankun; Wang, Chengzhi; Shiping, E; Li, Hengxuan

doi:10.1109/iaeac47372.2019.8998029

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…The first category, PI, was previously trained on the ImageNet data set [ 15 ], which comprises approximately one million images grouped in more than 1000 classes. For previously trained networks, the well-known architectures were VGG16 [ 17 ], VGG19 [ 18 ] and ResNet50 [ 19 ]. In the case of previously uninitiated parameters, the networks were trained directly on the image set of interest.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Colorectal Polyps Using Transfer Learning

Dulf

Bledea

Mocan

et al. 2021

Sensors

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Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection of Colorectal Polyps Using Transfer Learning

Dulf

Bledea

Mocan

et al. 2021

Sensors

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“…We add a PL between the de-blurred image and the original blurred image to achieve better deblurring results, as follows: Lp=‖ϕl(Fx)−ϕl(Ix)‖22,where ϕl(Ix) represents the features of the original blurred image at layer l of the pre-trained CNN, and ϕl(Fx) is the generated de-blurred image. In our experiments, we used the Conv3,3 layer of the VGG-19 network pre-trained on ImageNet 37 …”

Section: Methodsmentioning

confidence: 99%

“…In our experiments, we used the Conv 3;3 layer of the VGG-19 network pre-trained on ImageNet. 37 The complete objective loss function is the weighted sum of all losses between Eqs. ( 2) and ( 6) and is given as where…”

Section: Perceptual Lossmentioning

confidence: 99%

Flotation froth image deblurring algorithm based on disentangled representations

Huang,

Wang,

Cao

et al. 2024

J. Electron. Imag.

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The deblurring of flotation froth images significantly aids in the characterization of coal flotation and fault diagnosis. Images of froth acquired at a flotation site contain considerable noise and blurring, making feature extraction and segmentation processing difficult. We present an effective method for deblurring froth images based on disentangled representations. Disentangled representation is achieved by separating the content and blur features in the blurred image using a content encoder and a blur encoder. Then, the separated feature vectors are embedded into a deblurring framework to deblur the froth image. The experimental results show that this method achieves a superior deblurring effect on froth images under various conditions, which lays the foundation for the intelligent adjustment of parameters to guide the flotation site.

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“…As its name suggests, VGG19 includes 19 layers. Instead of using large filters, the VGG19 model uses multiple 3 × 3 filters per layer, with a size of 11 × 11 like Alexnet [32,33].…”

Section: Proposed Ensemble Modelmentioning

confidence: 99%

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

T黵k¹

2023

Computer Systems Science and Engineering

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Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the negative results caused by the virus and the treatment costs, the importance of fast and accurate diagnosis is clearly seen. In this context, deep learning methods appear as an extremely popular approach. In this study, a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease. In addition, in order to contribute to the literature, a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented. With this ensemble model design, quite remarkable results are obtained for the diagnosis of three and four-class Covid-19. The proposed model can classify normal, pneumonia, and Covid-19 with 92.6% accuracy and 82.6% for normal, pneumonia, Covid-19, and lung opacity.

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A Switch State Recognition Method based on Improved VGG19 network

Cited by 11 publications

References 3 publications

Automatic Detection of Colorectal Polyps Using Transfer Learning

Automatic Detection of Colorectal Polyps Using Transfer Learning

Flotation froth image deblurring algorithm based on disentangled representations

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

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